Databricks xOps - Pathway to Production ($200K)

Spyglass’s Databricks xOps - Pathway to Production ($200k+) is designed to help organizations manage and analyze large datasets using the Microsoft Databricks Lakehouse Platform. This service accelerates time-to-value, streamlines model development, and ensures scalability and operational efficiency in data workloads and AI/machine learning operations.

The solution includes optimized MLOps approaches and solutions on Microsoft Azure Databricks, covering data ingestion and preparation, model development and training, model deployment, monitoring and management, continuous integration and continuous deployment (CI/CD), and security and compliance.

Key benefits include reducing time-to-insight with efficient data ingestion and preparation processes, enabling rapid and reliable updates through CI/CD pipelines tailored to machine learning workflows. It streamlines model development by offering automated hyperparameter tuning and distributed training for high-performance models, maintaining reproducibility and consistency in experiments, and facilitating collaboration and innovation. Additionally, it ensures scalability and efficiency by leveraging Databricks' scalable infrastructure to handle large datasets and complex computations with ease, automating workflows to reduce manual intervention and increase operational efficiency.

The project follows a structured timeline over 12-16 weeks. Deliverables include a Microsoft Databricks Unity and Platform Governance Action Plan, a Microsoft Databricks Upskilling Plan for Microsoft Databricks engineers, Ops Squad, and end users, Microsoft Databricks training at levels 100 and 200, Microsoft Databricks xOps templates for pipelines, asset bundles, and GIT strategies, and xOps automation processes.

Spyglass Databricks xOps - Pathway to Production provides a robust solution for organizations looking to enhance their data management and AI capabilities. With a structured timeline and comprehensive deliverables, this service ensures accelerated time-to-value, streamlined model development, and scalability and efficiency in data workloads and AI/machine learning operations